http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Liu Jiansheng,Li Jiajia,Zhang Shu,Ding Mengbin,Yu Ningyue,Li Jingchao,Wang Xiuhui,Li Zhaohui 나노기술연구협의회 2022 Nano Convergence Vol.9 No.13
Infrared neural stimulation with the assistance of photothermal transducers holds great promise as a mini-invasive neural modulation modality. Optical nanoparticles with the absorption in the near-infrared (NIR) window have emerged as excellent photothermal transducers due to their good biocompatibility, surface modifiability, and tunable optical absorption. However, poor activation efficiency and limited stimulation depth are main predicaments encountered in the neural stimulation mediated by these nanoparticles. In this study, we prepared a targeted polydopamine (PDA)-coated gold (Au) nanoparticles with specific binding to thermo-sensitive ion channel as nanotransducers for second near-infrared (NIR-II) photo-stimulation of neurons in rats. The targeted Au nanoparticles were constructed via conjugation of anti-TRPV1 antibody with PEGylated PDA-coated Au nanoparticles and thus exhibited potent photothermal performance property in the second NIR (NIR-II) window and converted NIR-II light to heat to rapidly activate Ca 2+ influx of neurons in vitro. Furthermore, wireless photothermal stimulation of neurons in living rat successfully evoke excitation in neurons in the targeted brain region as deep as 5 mm beneath cortex. This study thus demonstrates a remote-controlled strategy for neuromodulation using photothermal nanotransducers.
LI, DI,XU, DUO,HEILES, CARL,PAN, ZHICHEN,TANG, NINGYU The Korean Astronomical Society 2015 天文學論叢 Vol.30 No.2
A growing body of evidence has been supporting the existence of so-called "dark molecular gas" (DMG), which is invisible in the most common tracer of molecular gas, i.e., CO rotational emission. DMG is believed to be the main gas component of the intermediate extinction region from Av~0.05-2, roughly corresponding to the self-shielding threshold of $H_2$ and $^{13}CO$. To quantify DMG relative to $H{\small{I}}$ and CO, we are pursuing three observational techniques; $H{\small{I}}$ self-absorption, OH absorption, and THz $C^+$ emission. In this paper, we focus on preliminary results from a CO and OH absorption survey of DMG candidates. Our analysis shows that the OH excitation temperature is close to that of the Galactic continuum background and that OH is a good DMG tracer co-existing with molecular hydrogen in regions without CO. Through systematic "absorption mapping" by the Square Kilometer Array (SKA) and ALMA, we will have unprecedented, comprehensive knowledge of the ISM components including DMG in terms of their temperature and density, which will impact our understanding of galaxy evolution and star formation profoundly.
A Comprehensive Review of Emerging Computational Methods for Gene Identification
( Ning Yu ),( Zeng Yu ),( Bing Li ),( Feng Gu ),( Yi Pan ) 한국정보처리학회 2016 Journal of information processing systems Vol.12 No.1
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches
( Ning Yu ),( Zeng Yu ),( Feng Gu ),( Tianrui Li ),( Xinmin Tian ),( Yi Pan ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.2
Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.